CAIMed Groups:

Knowledge graphs (KGs) are expressive data structures for integrating and describing heterogeneous data by unifying factual information and domain knowledge. However, under the Open World Assumption (OWA), the absence of facts does not imply falsity—only incompleteness. Inductive learning methods, particularly numerical techniques such as Knowledge Graph Embeddings (KGEs) and Graph Neural Networks (GNNs), are widely used for link prediction and classification tasks in KGs. These models excel at capturing latent patterns and exploiting structural properties at scale. Nevertheless, their performance can be significantly degraded by anomalies in KG representations—semantic inconsistencies and modeling artifacts that arise from unconstrained data integration. Such anomalies obscure the intended meaning of relations, introduce noise, and mislead numerical learning models. To address this issue, we introduce a normalization theory for KGs that enforces semantic consistency through normal forms. These forms restructure KGs to eliminate representational anomalies, ensuring that the data adheres to well-defined semantic constraints. We present VANILLA, a neuro-symbolic framework that combines symbolic rule learning, numerical inductive models, and constraint-based validation. By aligning inductive predictions with normalized, ontology-aware KG structures, VANILLA enables accurate and semantically grounded KG completion. Experimental results show that our approach significantly improves predictive performance while maintaining semantic integrity, demonstrating the value of normalization in hybrid KG learning systems. VANILLA is publicly available on GitHub https://github.com/SDM-TIB/VANILLA

CAIMed Groups:

Medical Image Segmentation (MIS) includes diverse tasks, from bone to organ segmentation, each with its own challenges in finding the best segmentation model. The state-of-the-art AutoML-related MIS-framework nnU-Net automates many aspects of model configuration but remains constrained by fixed hyperparameters and heuristic design choices. As a full-AutoML framework for MIS, we propose Auto-nnU-Net, a novel nnU-Net variant enabling hyperparameter optimization (HPO), neural architecture search (NAS), and hierarchical NAS (HNAS). Additionally, we propose Regularized PriorBand to balance model accuracy with the computational resources required for training, addressing the resource constraints often faced in real-world medical settings that limit the feasibility of extensive training procedures. We evaluate our approach across diverse MIS datasets from the well-established Medical Segmentation Decathlon, analyzing the impact of AutoML techniques on segmentation performance, computational efficiency, and model design choices. The results demonstrate that our AutoML approach substantially improves the segmentation performance of nnU-Net on 6 out of 10 datasets and is on par on the other datasets while maintaining practical resource requirements. Our code is available at this URL.